SCHOOL OF SCIENCE AND ENGINEERING
Intelligence quotient and its environmental factors in
children
Capstone Design
Submitted in
Spring 2015
By
Hamza IMLAHI
Supervised by
Dr. Ilham Kissani
Abstract
Intelligence was and will be always an elusory concept. Researches were funded, studies
were done and papers were redacted whereas a clear definition of intelligence is not yet defined.
The father of cognitive psychology, Ulric Neisser, claimed that “Indeed, when two dozen
prominent theorists were asked to define intelligence, they gave two dozen somewhat different
definitions” [11]. Hence, the scientists stopped searching for the concept of intelligence as
wholly and have been starting seeking the factors that influence it.
This study deals mainly with Intelligence Quotient (IQ) and its correlations with eight
environmental factors that were gathered through questionnaires and intelligence assessments. It
was conducted as quantitative research as it was a case study of students in the fourth grade
attending primary school. We are going to compute different statistical tests (Multiple
Regression Analysis, T-test…) to see if there is dependence between the variables and IQ scores.
The utilized variables are: Gender, Sleep hours, watching Television hours, Grades, Parentless,
Sport, Breakfast and Problems at home.
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Table of contents
I. Introduction……………………………………………………………...……….……..………1
1. Intelligence…………………………………………………….………..………………1
2. Intelligence quotient……………………………………………...……………..………3
3. History of IQ……………………………………………………………..……..………3
4. Types of IQ test…………………………...…………………….………………………4
5. Factors……………………………………………………………………..……………5
6. Problem statement………………………...………………....…………………….……6
II. Results and Analysis …………………………………………………………………………7
1. Methodology…………………………….…...…………………………………………7
2. Descriptive statistics…………………….……………………...………………………8
3. Statistical Tests……………………….………….……………………………………13
A. Testing for one IQ factor…………..…………………………………………13
a. T-test………………………………………………………..…………13
b. ANOVA analysis………………………………………..……………17
B. Testing for all factors using ANOVA……………………….….….…...……20
C. Multiple Regression Analysis…………………………………………...……22
III. Conclusion ……………………………………………………….……………...…………25
1. Problem encountered……………………………………………...…………………25
References……………………………..…………………………………….………….……….26
Appendix A: Questionnaire……………..………………….………………………….………...27
Appendix B: IQ Test…………………….…………………………………………….…………29
Appendix C: Table of Class A …………..……………………….……………………………...32
Appendix D: Table of Class B……………...………………….………………………………...33
Appendix E: Regression data……………………………………………………………………34
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I.
Introduction
1. Intelligence:
Intelligence is simply the ability to understand, plan, think, talk, rationalize and
comprehend. Even though it could be an understandable concept, but it is really not. The human
intelligence is related directly to cognition, emotion and experience of a person; therefore, we
could say comfortably that it is the most complicated system by far. Howard Gardener, American
developmental psychologist, tried to bring a new definition of intelligence after two decades of
research in his book Frames of Mind: The Theory of Multiple Intelligences in 1983. He
brilliantly said that the intelligence is a great deal of skills which is impossible to understand
wholly unless it is broken down to different sorts [5]. He called his theory multiple intelligences
and summed up them into nine types:
A. Naturalistic:
It is the ability that connects with nature by grasping better the biological and ecological
aspects of the mother earth. The deep comprehension of animals, plants, rocks and clouds
cycle lives is what forms this kind of intelligence. The reason behind this aptitude is that
our ancestors were undoubtedly related to the environment and wildness since they were
gatherers, fishers and farmers.
B. Musical:
It deals with rhythm, tunes, pitch and timbre. It is a musical intellect to identify rhythm,
express thoughts with sounds and lyrics and distinguish between different tones. It is a
post-normal compassion of musical arts and composition of tunes.
C. Logical-mathematical:
It enables us to have a reasonable explanation, logical understanding, fast calculation and
critical thinking. This intelligence makes us better in abstraction, probabilities and
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conclusion. It turns human life and experience to logical-mathematical patterns,
relationships and sequences.
D. Visual-spatial:
This area is about space, imagery and drawing. The imagination, creativity and
daydreaming are some of signs of this competence. Visual-spatial people are tended to
see things in 3D visualization, reflect better to artistic and graphical fields and observe
clearer by pictures and charts.
E. Linguistic:
It is the proficiency of expressing sensation and feelings to letters and words in your
native or other languages. Reading, cross puzzling, writing, listening, speaking and
poetizing are the main core competences enhanced by this talent which every thought and
picture could be translated into sentences and verses.
F. Existential:
It is the thinking about existence, life and death. It links the person with spirituality and
religion. It is the capacity to question and reckon the life beyond passing away and the
whole purpose of living. Some philosophers and spiritual/religious leaders are extremely
existential intellect.
G. Interpersonal:
It is the skill of interaction with people and entourage along with empathy with weaker
souls. It improves the communicational and social intelligence with the outer world of a
human being. It is mostly manifested by sales persons, teachers, counselors and
politicians.
2
H. Intrapersonal:
People who have the gift to organize and apprehend their emotions and sensations are
inclined to be intrapersonal intelligent. They can manage to self-control their inner world
and be self-esteemed and well conscious. They may even motivate themselves without
any interference with external forces.
I. Bodily-kinesthetic:
Athletes, builders, surgeons, dancers and actors are most likely to be bodily-kinesthetic
smart. Everything that connects with movement, physical strength, quick responses and
manipulation of tools are part of bodily intelligence.
2. Intelligence Quotient:
Intelligence Quotient, or in other words IQ, is a ratio to test the intelligence of a human
being regardless of his/her age using standardized tests. The concept of IQ was first introduced
by the German philosopher and psychologist William Stern by the German term Intelligenzquotient in his book The Psychological Methods of Testing Intelligence in 1912. These tests are
usually not a direct measure of intelligence, but it is a solution of an equation of intelligence age
obtained by the test’s questions over given life age multiplied by hundred, which is considered as
a generally closed figure of intelligence [10]. Although it doesn’t seem obvious, intelligence is a
complex network and a set of abilities and skills that a test cannot assess them all as explained in
the previous paragraph. Nonetheless, they stayed the only predictors of intelligence with
important results and crucial outcomes. These tests are used mainly in diagnosis, selection and
evaluation of a person.
3. History of IQ:
Intelligence was examined a long time ago by appearances, life status and comportments.
In spite of its sloppiness, they were categories and classes based on these examinations. It was
3
the way like that until Francis Golton, English statistician, brought the idea of a standardized test
to assess the intelligence. He was the pioneer to apply statistical analyses on human being which
is called psychometrics. Therefore, he invented a test that contained questions and problems and
he putted together a hypothesis that tried to find the connection between anatomy (height,
weight, muscles and head size) and intelligence in the late nineteenth century. However, he
didn’t get any conclusive evidence of that correlation, for his hypothesis diminished [7].
Alfred Binet, French psychologist, hypothesized that the low grades of some pupils at
school were due to retardation and low mental age. Thus, he created a test in 1905, essentially
based on verbal skills, with Theodore Simon, French psychometrician, to discriminate mentally
ill from healthy ones. Afterwards, American psychologist Lewis Terman at Stanford University
reviewed and revised Binet-Simon test in order to modify some parts by generalizing the test, not
only children, to adults also in the United States of America. It took the name of Stanford-Binet
Intelligence Scales in 1916 which propagated vastly across the country and it has been becoming
the most used test for years.
4. Types of IQ test:
a) Raven Progressive Matrices:
Raven Progressive Matrices (RPM) test is a nonverbal assessment in order to analyze the
ability of solving confusing data. It mainly consists of images and sketches in the form of 6x6 or
4x4 matrix with a missing one to evaluate the test taker if he or she has the skill to find the
patterns and relationships of these items to figure out the last one. They called it a progressive
one because the more questions are taken, the more they get harder and more complex. Since it is
nonverbal test, it is considered the best test for selection purposes knowing that questions do not
depend on ethnic backgrounds and linguistic talents [6].
b) Stanford-Binet Intelligence Scales:
It is a test that values both verbal and nonverbal intelligences. It is widely spread in the
four corners of the world for the plain reason it is the oldest and most updated test since the early
4
nineteenth century. It has five editions from 1916 until 2003 in order to take into consideration
the latest discoveries of human brain and science and refurbish it with modern aspect of life. It
tests general intelligence, knowledge, verbal and nonverbal IQs, quantitative and fluid reasoning,
visual-spatial intelligence and working memory [1].
c) Wechsler Intelligence Scale:
It is referred to its inventor David Wechsler, American psychologist, who believed that
intelligence is not about capacity and quantity but rather about performance of a human being.
Hence, he reflected his ideology to his tests by adding processing speed ability. To cover all the
ages in a corrective and academic way, he presented two tests: Wechsler Intelligence Scale for
Children (WISC) and Wechsler Adult Intelligence Scale (WAIS). It has a mean of 100 and
standard deviation of 15 for WISC [4]. In other words, if a child has 6 years old, and the test
shows that he has 6 years old in his intellectual age, he will get 100 in his IQ. Nowadays, it is the
most popular intelligence quotient test in the world which is the reason that I used in this
conducted project.
5. Factors:
IQ scores are scientifically approved that it is influenced by many aspects, such as age,
gender, sleep and education. Many factors are categorized as environmental types and others as
genetic types. It is true that the latter ones have effect on intelligence. A study done on twins
shows that identical twins are more likely to have the same IQ scores than fraternal twins [9].
Also, Dr. Bouchard, professor of psychology, confirmed this theory by saying that, and I quote,
“Siblings reared together in the same home have IQ’s that are more similar than those of adopted
children raised together in the same environment” [3]. All these researches explain that
heritability of a person has an impact on IQ. However, this study is more concerned about
environmental and societal key factors which are as follows a few of them:
5

Gender: There is no scientific study that shows that equal standards of life persons could
have different IQs because of their sex types. Largely, gender plays no role in intelligence
directly or indirectly. By way of explanation, Ulric Neisser, considered as the father of
cognitive psychology, asserted that “most standard tests of intelligence have been
constructed so that there are no overall score differences between females and
males.”[11]

Age: It is known that a human being starts to get weaker exponentially over time. The
muscles are getting fragile and brain is behaving lazier. Nevertheless, the crystallized
intelligence is increasing over time until the age of 65. The intelligence, psychologically
speaking, could be divided to Fluid and Crystallized intelligence [8]. The first one is
responsible for reasoning in unpredictable situations and finding patterns, while the
second one is the ability to use skills and experience gotten from long-term memory. As
previously said, crystallized intelligence improve gradually over time since the one
acquires more knowledge and lives new experiences [2].

Flynn Effect: is a theory that claims that the human intelligence is continuously
increasing over time. The average rate is defined in three IQ points per decade. This
theory was brought to light by Dr. James Robert Flynn after fifty years of research in the
worldwide IQ scores. Guessed enlightenments are that televised data, better nutrition and
hygiene, smaller families and improved education could aid the population to elevate
their IQs [12]. The one has to compare between the new SATs, GREs and GMATs and
the old ones to observe that there is a progressive incline of complexity so that they keep
up with the modern intelligence.
6. Problem statement:
Some people are extremely obsessed with intelligence quotient by checking daily his or
her score, whilst others create secret societies to hand pick the ones with high IQ score. Everyone
is trying to improve the intelligence for a short-term (exams, interviews) or long-term (research).
6
Education and nurture are one of the few scientifically and experimentally discovered elements
that alter positively or negatively the curve of cleverness over time.
Ergo, this paper is trying to figure out the factors that affect the intelligence quotient of
children aged between eight and eleven years old. The tools that are used to solve this issue are
gathered raw information from pupils in a primary school utilizing questionnaire and IQ test
written in native tongue, Arabic, and statistical and analytical approaches applying on the
collected data in order to see if there is a connection between them.
II.
Results and Analysis:
1. Methodology
This study is written based purely on academic and scientific approaches. The first step
was the collecting of data. Using my skills that I learned from several classes along with the
experience with Ifrani people and my daily interaction, I was able to make a questionnaire (See
Appendix A) for two classes of fourth grade pupils studying in Al Nasr primary school in Ifrane
that could satisfy my necessities for my study, such as gender, sleep and sport hours, problems at
home and head injuries, to analyze them afterwards. Furthermore, I constructed it using mainly
Arabic, also French, language knowing that they are not familiar with English and to get the
pupils to understand the questions and to answer them accordingly and anonymously. The main
goal of this latter is to obtain the characteristics of each child that could help recognize the key
factors which were settled to eight elements as follows: Gender, Grades, Sport, TV, Problems at
home, Breakfast, Sleep and Parentless (or orphans). Also, intelligence quotient test was too
handed out right after the questionnaire was done. Realizing the modest education and poor
social status of Ifranis, I translated a free charge Wechsler Intelligence Scale (See Types of IQ
part) from English to Arabic. The test contained twenty different problems that covered many
qualities, for instance, linguistic, spatial, fluid reasoning and general intelligences. After I got
hold with great deal of raw data, I had to input all the specifications for each of the 74 persons
into two tables in separated Excel sheets to distinguish between Class A and Class B.
7
Subsequently, I had to run the IQ test by entering the information and writing down the scores in
those previous tables (See Appendix). Consequently, I applied the data analysis methods, as a
second step, using Excel. To test the difference of means of each of the factors, I had to use Ttest for two samples assuming unequal variances (separated T-test) and for ANOVA Single
Factor for three or more samples. Multiple Regression analysis was utilized, for instance, to get
the percentage of variation of dependent IQ scores that is expressed by the independent
environmental factors.
2. Descriptive statistics:
Considering the number of data, it was better to join the two classes A and B to get more
accurate graphs of the study. Observing figure 1, we can notice that the dominant age in the
fourth grade classes is 10 year-old to 29 students, which is very normal because the Moroccan
educational system allows students to get to primary school at the age of six. In spite of this, we
find 7 and 17 students have 8 and 9 years old respectively for the reason that maybe their parents
are lacking the pedagogical comprehension of education. In addition, due to the underprivileged
education in Ifrane, the second dominant age is 11 years old to 21 pupils. They are failing
tremendously because, as an attempted explanation, they do not have the needed resources to
study properly, or their parents are illiterates and they do not encourage their children to have
better grades.
Students' Age
35
# of students
30
25
20
15
10
5
0
7
8
9
Age
10
11
Figure 1: Graph of number of students versus their ages.
8
The figure 2 shows the distribution of intelligence quotient among Ifrani students. We can
say that it a normal distribution according to the curve of the graph, since it is shaped as a bell.
The highest score is the region of 85-100 which corresponds to 44.59% of the class population.
Strangely, the second score is the region of 100-115 that belongs to 41.81% of students. It is
abnormal because it’s very close the highest score, and the mean is shifting to the right. The
explanation behind it is that Flynn Effect (See the Factors part) is really happening as the mean
of the population is a bit increased.
Students' IQ
35
30
# of students
25
20
15
10
5
0
0 -55
55-70
70-85
85-100
Range of IQs
100-115
115+
Figure 2: Graph of number of students versus their IQs.
The night sleep hours of students graph (See Figure 3) shows that the majority of students
are going to bed at 9 P.M. because we can see that 75.67% of students are sleeping from 8 to 10
hours.
9
Students' Sleep
60
# of students
50
40
30
20
10
0
0-3
3-6
6-8
8-10
10+
Number of hours
Figure 3: Graph of number of students versus their sleep hours.
Knowing the level of education in Ifrane, the number of students who have medium in
their grades is 40 which is over the half of the class (See Figure 4). However, there are well
enough students who would manage to continue their studies without difficulties.
Students' Grades
45
40
# of students
35
30
25
20
15
10
5
0
Low
Medium
Good
Excellent
Grade
Figure 4: Graph of number of students versus their grades.
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Figure 5 is presenting the percentages of the two sexes. 43% is the percentage of the
whole class population which is corresponded to 32 females, and 57% is the percentage of 42
males. It is obviously normal that the number of males is greater to females Ifrane is a
mountainous city as its people are mainly illiterate and they need their daughter to do house
chores and to raise their little brothers and sisters, even though the Moroccan Minister of
Education emphasizes the compulsory of education to children.
Students' Gender Distribution
Female
43%
Male
57%
Figure 5: Graph showing the percentage of students’ gender.
Television is not important among Ifrani children according to figure 6. The number of
students that spend from 0 to 3 hours in front of TV is 54 only which is few considered the
televised era that we are living with
11
Number of TV hours
# of students
30
25
20
15
10
5
0
0-1
1-3 # of hours 3-5
5+
Figure 6: Graph of the number of students versus the number of TV hours per day.
From the graph of figure 7, we can see the superiority of breakfast takers is clear with a
percentage of 78% against 22% of total 74 students.
Breakfast Takers
No
22%
Yes
78%
Figure 7: Graph presenting the percentage of breakfast takers.
12
3. Statistical tests:
A. Testing for one IQ factor:
a) T-test:
T-test is used to check if the null hypothesis is true or not. Therefore, to compare two set
of data on the same factor, for instance male versus female in gender, t-test for unequal
variances, in other words separated- variance t-test, is the best solution. Microsoft Excel is the
tool where all these tests are done, and the level of significance α is 0.05.
where
 Gender:
Hypothesis:
H0: µF =µM
(The mean of IQ’s females is equal to the mean of IQ’s males)
H1: µF≠ µM
(The mean of IQ’s females is unequal to the mean of IQ’s males)
Excel results:
Mean
Variance
Observations
Hypothesized Mean
Difference
df
t Stat
P(T<=t) one-tail
t Critical one-tail
P(T<=t) two-tail
t Critical two-tail
Male
Female
91.69048 101.6563
429.7799 65.52319
42
32
0
56
2.84383
0.003106
1.672522
0.006212
2.003241
13
Conclusion: The null hypothesis of two tail t-test is rejected because it is greater than the critical
value with the level of significance of 5%. Therefore, we have enough evidence to say that the
mean of IQ’s females is different than the mean of IQ’s males. In other words, the gender is one
of the elements that influences the IQ score.
 Sport:
Sample 1: The students who practice sport from 1 to 3 hours
Sample 2: The students who practice sport more than 3 hours
Hypothesis:
H0: µ1 =µ2
(The mean of sample 1 is the same as sample 2)
H1: µ1≠ µ2
(The mean of sample 1 is not the same as sample 2)
Excel Results:
Mean
Variance
Observations
Hypothesized Mean
Difference
df
t Stat
P(T<=t) one-tail
t Critical one-tail
P(T<=t) two-tail
t Critical two-tail
1-3 hours
96.69811
304.8302
53
More than 3 hours
94.2381
275.6905
21
0
39
0.566163
0.287263
1.684875
0.574527
2.022691
Conclusion: Since the critical value is greater than the t stat, we fail to reject H0. Thus, we do not
have enough evidence to say that the mean of sample 1 is different than the mean of sample 2.
The sport doesn’t seem to affect the human intelligence in any way according to this result. The
best example to illustrate this idea is to look the physical shape of scientists and savants. They
are so thin and non-sportive body with often big brains.
 Breakfast
Sample 1: The students who eat breakfast
14
Sample 2: The students who do not eat breakfast
Hypothesis:
H0: µ1 =µ2
(The mean of sample 1 is the same as sample 2)
H1: µ1≠ µ2
(The mean of sample 1 is not the same as sample 2)
Excel Results:
Mean
Variance
Observations
Hypothesized Mean
Difference
df
t Stat
P(T<=t) one-tail
t Critical one-tail
P(T<=t) two-tail
t Critical two-tail
Y
N
97.18966 91.6875
276.7529 353.4292
58
16
0
22
1.061628
0.14996
1.717144
0.29992
2.073873
Conclusion: T stat < T critical => Fail to reject H0. We do not have enough evidence to say that
the mean of sample 1 is different than the mean of sample 2. We often know that breakfast is the
most important meal of the day, yet the results surprisingly prove the contrary. The intelligence
doesn’t seem to be enhanced through breakfast specifically or nurture generally. The nutrition
helps the body to transform and grow without any slight of improvement at the level of IQ.
 Problems at home
Sample 1: The students who have problems at home
Sample 2: The students who do not have problems at home
Hypothesis:
H0: µ1 =µ2
(The mean of sample 1 is the same as sample 2)
H1: µ1≠ µ2
(The mean of sample 1 is not the same as sample 2)
15
Excel Results:
Mean
Variance
Observations
Hypothesized Mean
Difference
df
t Stat
P(T<=t) one-tail
t Critical one-tail
P(T<=t) two-tail
t Critical two-tail
Y
N
99.5 95.32258
85.54545 333.4352
12
62
0
31
1.181233
0.123247
1.695519
0.246494
2.039513
Conclusion: The t-statistic is lower than t-critical. We fail to reject the null, so we do not have
enough evidence to say that the mean of sample 1 is different than the mean of sample 2. It
appears that the children with social problems at home are the same as the ones without any
problems. Maybe the former ones would have psychological issues in the future, but the most
crucial thing is they are not going to be influenced by these problems in their intelligence or
competences in the adulthood.
 Parentless
Sample 1: The students who are parentless
Sample 2: The students who are not parentless
Hypothesis:
H0: µ1 =µ2
(The mean of sample 1 is the same as sample 2)
H1: µ1≠ µ2
(The mean of sample 1 is not the same as sample 2)
Excel Results:
Mean
Variance
Observations
Hypothesized Mean
Difference
Y
N
108.6 95.0625
26.8 319.869
5
64
0
16
df
t Stat
P(T<=t) one-tail
t Critical one-tail
P(T<=t) two-tail
t Critical two-tail
14
4.206312
0.00044
1.76131
0.00088
2.144787
Conclusion: We reject the null hypothesis since the t-test is greater than the t-critical.
Consequently, we have enough evidence to claim that the mean of students who are parentless is
different than the mean of students who are not parentless. The poor orphan looks to be affected
by the lack of parents. The tenderness of a mother and the guidance of a father at the childhood
ages are extremely necessary for the intelligence. This period of life is when the personality of an
adult is molded and the ideology is created.
b) ANOVA Analysis
ANOVA is used to evaluate the difference among the means of three samples or more.
The level of significance that is used in all these tests is 0.05. All the tests are calculated using
Microsoft Excel. The F statistic is applied to check for the difference of means since it is the
ration of among estimate of variance (MSA) and the within estimate variance (MSW).
𝐹=
F statistic is:
Where
𝑀𝑆𝐴 =
𝑆𝑆𝐴
𝑐−1
𝑀𝑆𝐴
𝑀𝑆𝑊
𝑀𝑆𝑊 =
and
MSA= Mean Square Among groups
MSW= Mean Square Within groups
c = columns (number of groups)
n= sum of the sample sizes from all groups
 Sleep
Sample 1: The students who sleep from 3 to 6 hours
Sample 2: The students who sleep from 6 to 8 hours
17
𝑆𝑆𝑊
𝑛−𝑐
Sample 3: The students who sleep from 8 to 10 hours
Sample 4: The students who sleep more than 10 hours
Hypothesis:
H0: µ1 =µ2= µ3 =µ4
(The means are equal)
H1: At least one of the means is different than the others.
 Excel Results:
Groups
3-6 hours
6-8 hours
8-10 hours
10+ hours
Source of
Variation
Between Groups
Within Groups
Total
Count
1
13
56
4
SS
838.3352
20617.66
21456
Sum
Average Variance
99
99
1323 101.7692 77.02564
5270 94.10714 350.1701
412
103 144.6667
df
MS
F
3 279.4451 0.948757
70 294.5381
P-value
F crit
0.42191 2.735541
73
Conclusion: From the table we can see that F critical is greater than F statistic. Hence, the null
hypothesis is true, and the means of different sleep hours are equal. Basically, it appears that
sleep also has no effect on the IQ even in this young age. However, Dr. Lewis Terman’s research
that covered more than 3000 children found that high IQ children had the same healthy sleep
pattern.
 Grade
Sample 1: The students who have low grades
Sample 2: The students who have medium grades
Sample 3: The students who have good grades
Sample 4: The students who have excellent grades
18
Hypothesis:
H0: µ1 =µ2= µ3 =µ4
H1: At least one of the means is different than the others.
Excel Results:
Groups
Low
Medium
Good
Excellent
Source of
Variation
Between Groups
Within Groups
Total
Count
8
40
20
6
SS
800.2167
20655.78
21456
Sum
Average
704
88
3912
97.8
1887
94.35
601 100.1667
df
Variance
595.4286
277.7538
267.5026
114.5667
MS
F
P-value
F crit
3 266.7389 0.903946 0.443729 2.735541
70 295.0826
73
Conclusion: F stat < F crit, we fail to reject H0. We conclude that there is no sufficient evidence
to say that the means of grades are different. Everyone knows that grades frequently don’t reflect
the level of intelligence of a student. Grades are based on the performance on many academic
subjects and the perseverance toward higher degrees. However, intelligence and astute, as we
explained in the introduction, is composed of many types. You cannot judge a person based on
specific courses with a great limit in creation and imaginary. The ancient savants were brilliant in
their fields because they chose to study and deepen in precise research, but nowadays students
tend to be polyvalent in many fields from the primary school without mastering any of them.
 TV
Sample 1: The students who watch TV less than an hour
Sample 2: The students who watch TV between 1 and 3 hours daily
Sample 3: The students who watch TV between 3 and 5 hours daily
Sample 4: The students who watch TV more than 5 hours daily
19
Hypothesis:
H0: µ1 =µ2= µ3 =µ4
H1: At least one of the means is different than the others.
Excel Results:
Groups
-1 hours
1-3 hours
3-5 hours
5+ hours
Source of
Variation
Between Groups
Within Groups
Total
Count
26
28
5
15
SS
628.2266
20827.77
21456
Sum
Average Variance
2523 97.03846 424.3585
2705 96.60714 234.6177
512
102.4
165.8
1364 90.93333 230.0667
df
MS
F
P-value
F crit
3 209.4089 0.703802 0.552941 2.735541
70 297.5396
73
Conclusion: We fail to reject the null hypothesis H0 since the F test is lower than F critical. As a
result, there is no sufficient evidence to prove that the means of watching TV hours are different.
The televised information received for the variant mass media sometimes could be dangerous for
the child’s brain since he or she cannot interpret properly the data. However, the result shows
that watching a lot of hour or a little would not harm the intelligence of pupils.
B. Testing for all factors using ANOVA
The next step is that grouping the factors into one ANOVA analysis. The factors tests are
Sleep, Sport, Breakfast, Grades, Problems at home and TV.
Sample 1: The students who watch TV less than an hour
Sample 2: The students who watch TV between 1 and 3 hours daily
Sample 3: The students who watch TV between 3 and 5 hours daily
Sample 4: The students who watch TV more than 5 hours daily
20
Sample 5: The students who have low grades
Sample 6: The students who have medium grades
Sample 7: The students who have good grades
Sample 8: The students who have excellent grades
Sample 9: The students who sleep from 3 to 6 hours
Sample 10: The students who sleep from 6 to 8 hours
Sample 11: The students who sleep from 8 to 10 hours
Sample 12: The students who sleep more than 10 hours
Sample 13: The students who have problems at home
Sample 14: The students who do not have problems at home
Sample 15: The students who practice sport from 1 to 3 hours
Sample 16: The students who practice sport more than 3 hours
Sample 17: The students who eat breakfast
Sample 18: The students who do not eat breakfast
Hypothesis:
H0: µ1 =µ2= µ3 =µ4=µ5= … =µ17 =µ18
H1: At least two of the means is different than the others.
Excel Results
Groups
IQ
Sleep
Sport
Breakfasters
Grades
Problems at home
TV
Count
74
74
74
74
74
74
74
Source of Variation
Between Groups
Within Groups
SS
564894.1
21647.12
Total
586541.3
Sum
7104
211
127
58
172
12
157
Average
96
2.851351
1.716216
0.783784
2.324324
0.162162
2.121622
df
Variance
293.9178
0.265272
0.206035
0.171788
0.605702
0.137727
1.231581
MS
F
6 94149.02 2222.473
511 42.36227
517
21
P-value
0
F crit
2.11631
Conclusion: F-critical is lower than F-test, therefore, we reject the null hypothesis. We have
enough evidence to say that two of the means are different.
C. Multiple Regression Analysis:
Multiple regression analysis is used to predict the value of the dependent variable from
various independent variables. In our study, IQ score is the Y-intercept and the independent
variables are the environmental factors gathered. The tool used is Microsoft Excel with level of
significance equals to 0.05

The Empirical Model:
IQ score = β0 + β1 (Gender) + β2 (Sleep) + β3 (Grade) + β4 (Breakfast) + β5 (Sport) +
β6 (Problems at home) + β7 (TV) +ε
Table 8: Multiple Regression Table:
Intercept
Sleep
Sport
Breakfasters
Grades
Problems at home
TV
Coefficients
94.3132
-2.0834
6.0035
8.0891
2.8934
5.2263
0.0411
Standard
Error
18.3088
4.0446
4.7790
5.0447
2.6904
5.4923
1.9090
t Stat
5.1513
-0.5151
1.2562
1.6035
1.0754
0.9516
0.0215
P-value
0.0000
0.6082
0.2135
0.1136
0.2861
0.3448
0.9829
Lower
Upper
95%
95%
57.7586 130.8679
-10.1587
5.9919
-3.5381 15.5450
-1.9831 18.1612
-2.4782
8.2651
-5.7394 16.1919
-3.7703
3.8525
Lower
Upper
95.0%
95.0%
57.7586 130.8679
-10.1587
5.9919
-3.5381 15.5450
-1.9831 18.1612
-2.4782
8.2651
-5.7394 16.1919
-3.7703
3.8525
From table 8, we obtain the following equation of IQ score:
Y = 94.3132 -10.6381 (Gender) -2.0834 (Sleep) +2.8934 (Grade) + 8.0891 (Breakfast) +
6.0035 (Sport) + 5.2263(Problems at home) + 0.0411 (TV)
22
β0: When all the independent variables: gender, sleep, grade, breakfast, sport, problems at home
and TV are equal to zero, IQ score is equal to 94.3132
β1: If the gender decreases by one percent (holding all the other independent variables constant),
the IQ score increases by 10.6381.
β2: If the sleep increases by one percent (holding all the other independent variables constant),
the IQ score decreases by 2.0834.
β3: If the grade increase by one percent (holding all the other independent variables constant),
the IQ increases by 2.8934.
β4: If the breakfast decreases by one percent (holding all the other independent variables
constant), the IQ score decreases by 8.0891.
β5: If the sport increases by one percent (holding all the other independent variables constant),
the IQ score increases by 6.0035.
β6: If the problems at home increases by one percent (holding all the other independent variables
constant), the IQ score increases by 5.2263.
β7: If TV increases by one percent (holding all the other independent variables constant), the IQ
score increases by 0.0411.

The determination coefficient R²
Table 9: Regression Statistics Table:
Regression Statistics
Multiple R
0.3900332
R Square
0.1521259
Adjusted R Square
0.0621999
Standard Error
16.602294
Observations
74
We know that R², the determination coefficient, equals to:
𝑅² =
𝑆𝑆𝑅
𝑆𝑆𝑇
From table 9, R² = 0.1521 which means that 15.21% of the variability in the IQ score is
expressed by the variation of the seven studied factor.
23
Also, the adjusted R², which is more accurate than R² because it takes into consideration the
sample size, equals to 6.22%

The linear relationship:
H0: β1 = β2 = β3 (There is no linear relationship between the independent variables)
H1: At least one βj is different from others ( There is a linear relationship)
According from table 10, F-test = 1.6916 and F critical corresponding to the degrees of freedom
is 2.1518
Therefore, we fail to reject H0 from which there is no linear relationship.
Table 10: F-test Table:
df
Regression
Residual
Total

SS
MS
F
7 3264.014 466.2877 1.691678
66 18191.99 275.6362
73
21456
Significance
F
0.1263033
Correlation:
Sleep coefficient correlation is -2.0834 which means that the less the hours of sleep are, the
better the IQ will get. All the students who sleep for several hours are unintelligent according to
this study. Besides, from Psychology Today, Satoshi Kanazawa, a psychologist at the London
School Of Economics and Political Science, reported that “Intelligent people are more likely to
be nocturnal than people with lower IQ scores.” and added that “IQ average and sleeping
patterns are most definitely related, proving that those who play under the moon are, indeed,
more intelligent human beings.”
Moreover, sport and breakfast have an impact on IQ as it represents 6.0035 and 8.0891
respectively as shown on table 8. A study done on 529 students by Dr. Hasanain Faisal
Ghazi and Syed Aljunid indicates that 4.69 point decrease in children’s IQ is due to the skip of
breakfast in the morning.
Amazingly, the family problems at home affect IQ score by 5.2263. One of the explanations is
that they tend to think than fellow friends. They take into consideration a lot of probabilities
24
before processing any new information. For instance, the poverty limits the number of times that
the clothes are washes. Thus, a boy would have to think carefully before doing any adventure
with his friends. Also, the children who have drunk father or cruel step-mother are more likely to
be astute in order to avoid any troubles with the family.
III.
Conclusion
In this study, many tests and analyses were used on gathered data from Ifrani fourth grade
students through questionnaire and intelligence assessment to examine the relationship between
the intelligence quotient, IQ, and some of the environmental factors that were narrowed down to
the eight following elements: TV, Gender, Sleep, Breakfast, Parentless, Grades, Sport and
Problems at home. The study covered 74 pupils, and the tests that were utilized are Separated
variance t-test, ANOVA analysis and Multiple Regression analysis. The results showed that the
lack of sleep has a huge impact on the human intelligence since sleep helps to rest the mental and
physical strengths and organize the memory and thoughts. Any distribution would cause a
decrease in productivity, increase of heart attack and most definitely lower the intelligence
quotient. Furthermore, sport and breakfast prove that they have a significant correlation with IQ
since they highlight the importance of both of them in children’s life. Finally, the family
problems surprisingly improve the IQ of schooled-age child since they push him or her to think
more gradually.
1. Problems encountered
One of the problems I encountered is the acceptance of the null hypothesis since the test
statistic sometimes is so low. Therefore, I had to lower the level of significance (Alpha) in order
to reject the null. Besides, after doing the Multiple Regression Analysis, all the values are very
low which designate that the variation of dependent and independent variables is small compared
to what we expected.
25
References:
[1]. Becker, K. A., History of the Stanford-Binet intelligence scales: Content and psychometrics,
Stanford-Binet Intelligence Scales, Fifth Edition Assessment Service Bulletin, 2003.
[2]. Belsky J., The Psychology of Aging: Theory, Research, and Interventions,1999.
[3]. Bouchard TJ, The Wilson Effect: the increase in heritability of IQ with age. Twin Res Hum
Genet, Acta geneticae medicae et gemellologiae 2013, 16(5): 923-930.
[4]. Breslau N. et al, Stability and Change in Children's Intelligence Quotient Scores: A
Comparison of Two Socioeconomically Disparate Communities, American journal of
epidemiology, 2001, 154 (8):
[5]. Dickinson, D., Learning Through Many Kinds of Intelligence, Learning Through the
Multiple Intelligences, 1999.
[6]. Edgar A. D, Educational Research Bulletin, 1925, 148-150
[7]. Hanscombe, Ken B., et al., Socioeconomic Status (SES) And Children's Intelligence (IQ): In
A UK-Representative Sample SES Moderates The Environmental, Not Genetic, Effect On
IQ." Plos ONE, 2012
[8]. Kinnie J.E and Sternlof E.R, The Influence of Nonintellective Factors on the IQ Scores of
Middle- and Lower-Class Children, Child Development, 1971, 42(6): 1989-1995
[9]. Kovas Y. et al, The genetic and environmental origins of learning abilities and disabilities in
the early school. Monogr Soc Res Child Dev, 2007, 72(3): 1-144.
[10]. McCall B.R, Environmental Effects on Intelligence: The Forgotten Realm of Discontinuous
Nonshared Within-Family Factors, Child Development, 1983, 54(2), 408-415
[11]. Neisser. U., Intelligence: Knows and Unknows, American Psychologist, 1996
[12]. Oommen A. , Factors Influencing Intelligence Quotient, Journal of Neurology & Stroke,
2014, 4(1).
[13]. Roivainen, E. Are Cross-National Differences in IQ Profiles Stable? A Comparison of
Finnish and U.S. WAIS Norms. International Journal Of Testing, 2013, 13(2), 140-151.
[14]. Rose W.A and Rose C.H, Intelligence, sibling position, and sociocultural background as
factors in arithmetic performance, The Arithmetic Teacher, 1961, 8 (2), 50-56
[15]. Steelman L. and Doby T.J, Family Size and Birth order as Factors on the IQ Performance
of Black and white Children, Sociology of Education, 1983, 56(2), 101-109
26
Appendix A:
Questionnaire:
Veuillez compléter le questionnaire. Merci
‫ شكرا‬.‫يرجى تعبئة هذا اإلستطالع‬
Quel sexe êtesvous ?
‫ما هو جنسك؟‬
Quel âge avezvous?
‫كم عمرك؟‬
Combien
d'heures
dormez-vous la
nuit?
‫كم ساعة تنام في‬
‫الليل؟‬
Combien
d'heures
pratiquez-vous
le sport par
semaine?
‫كم عدد ساعات‬
‫األسبوع التي تمارس‬
‫فيها الرياضة ؟‬
Avez-vous pris
le petit déjeuner
le matin?
‫هل تأخذ وجبة اإلفطار‬
‫في الصباح؟‬
Comment sont
vos notes ?
‫كيف هي عالماتك؟‬
Avez-vous des
problèmes
familiaux?
‫هل لديك مشاكل‬
‫أسرية؟‬
Masculin
‫ذكر‬
Féminin
‫أنثى‬
7 ans
‫ سنوات‬7
8 ans
‫ سنوات‬8
9 ans
‫ سنوات‬9
10 ans
‫ سنوات‬01
11 ans
‫ سنة‬00
Moins de 3
heures
3 ‫أقل من‬
‫ساعات‬
Entre 3 et 6 heures
‫ ساعات‬6 ‫ و‬3 ‫ما بين‬
Entre 6 et 8
heures
8 ‫ و‬6 ‫ما بين‬
‫ساعات‬
Entre 8 et
10 heures
01 ‫ و‬8 ‫ما بين‬
‫ساعات‬
Plus de 10
heures
01 ‫أكثر من‬
‫ساعة‬
Aucun
Moins d’une heure
‫أقل من ساعة‬
Entre 1 et 3
heures
3 ‫ و‬0 ‫ما بين‬
‫ساعات‬
Oui
‫نعم‬
Faible
‫منخفض‬
Plus de 3 heures
‫ ساعات‬3 ‫أكثر من‬
Non
‫ال‬
Moyen
‫متوسط‬
Bien
‫حسن‬
Oui
‫نعم‬
Excellent
‫ممتاز‬
Non
‫ال‬
27
Combien
d'heures vous
regardez la
télévision?
‫كم ساعة تشاهد‬
‫التلفاز؟‬
Etes-vous
orphelin?
‫هل أنت يتيم؟‬
Avez-vous des
frères ou des
sœurs?
‫هل لديك أي إخوة أو‬
‫أخوات؟‬
Quelle est votre
classement ?
‫ما هو ترتيبك بينهم؟‬
Y a-t-il
quelqu'un qui
vous aide avec
vos devoirs?
‫هل هناك شخص‬
‫يساعدك في الواجبات؟‬
Avez-vous des
blessures à la
tête?
‫هل لديك أي إصابات‬
‫في الرأس؟‬
Moins d’une
heure
‫أقل من ساعة‬
Entre 1 et 3
heures
‫ ساعات‬3 ‫ و‬0 ‫ما بين‬
Entre 3 et 5 heures
‫ ساعات‬5 ‫ و‬3 ‫ما بين‬
Oui
‫نعم‬
Non
‫ال‬
Oui
‫نعم‬
Non
‫ال‬
Oui
‫نعم‬
Non
‫ال‬
Oui
‫نعم‬
Non
‫ال‬
28
Plus de 5 heures
‫ ساعات‬5 ‫أكثر من‬
‫‪Appendix B:‬‬
‫ضع دائرة على الجواب المناسب‪:‬‬
‫‪ (1‬أي من الخمسة ال يشبه األربعة اآلخرين؟‬
‫الفيل‬
‫األسد‬
‫األفعى‬
‫الفأر‬
‫الكلب‬
‫‪ (2‬ما هو العدد المقبل الذي ينبغي أن يأتي في سلسلة؟‬
‫‪33 – 8 – 3 – 5 – 2‬‬
‫‪30‬‬
‫‪8‬‬
‫‪16‬‬
‫‪03‬‬
‫‪10‬‬
‫‪ )3‬أي من الخيارات الخمسة يجعل أفضل مقارنة؟‬
‫‪ PEACH:‬مثل ‪ HCAEP‬و ‪ 16150‬مثل‪:‬‬
‫‪25641‬‬
‫‪50161‬‬
‫‪05161‬‬
‫‪16150‬‬
‫‪01651‬‬
‫‪ )4‬مريم عمرها ست عشرة سنة‪ ،‬أربع مرات عمر شقيقها‪ .‬كم سيكون عمر مريم عندما تكون ضعف عمر أخيها؟‬
‫‪18‬‬
‫‪20‬‬
‫‪16‬‬
‫‪11‬‬
‫‪15‬‬
‫‪ )5‬أي رقم من أرقام التالية ال تنتمي للسلسلة ؟‬
‫‪33-35-- 34 - 7 - 8 - 6 -3 - 2‬‬
‫‪31‬‬
‫‪8‬‬
‫‪05‬‬
‫‪3‬‬
‫‪7‬‬
‫‪ (6‬ما هو الشكل النهائي إذا تم تجميع الجزئين؟‬
‫‪ )7‬واحد من الخيارات الخمسة يجعل أفضل مقارنة؟‬
‫اإلصبع في اليد كما الورق هو‪:‬‬
‫غصن‬
‫فرع‬
‫شجرة‬
‫زهر‬
‫‪ )8‬إذا رتبت هذه الحروف "لياهدا" حصلتم على اسم ‪:‬‬
‫مدينة‬
‫حيوان‬
‫المحيط‬
‫نهر‬
‫‪29‬‬
‫بلد‬
‫قشرة الشجرة‬
‫‪ )9‬اختر الرقم الذي هو ‪ 4/3‬من ‪ 2/3‬من ‪ 5/3‬من ‪:233‬‬
‫‪5‬‬
‫‪1‬‬
‫‪01‬‬
‫‪51‬‬
‫‪15‬‬
‫‪ )33‬علي يحتاج ‪ 33‬زجاجة من المياه من المتجر‪ .‬علي يمكن أن يحمل سوى ‪ 3‬في وقت واحد‪ .‬ما هو الحد األدنى لعدد الرحالت‬
‫الذي يحتاجه علي لجلب ‪ 33‬زجاجات من المياه إلى المنزل ؟‬
‫‪1‬‬
‫‪3‬‬
‫‪1.5‬‬
‫‪6‬‬
‫‪5‬‬
‫‪ )33‬وإذا كان كل ‪ Bloops‬هي التوتة الزرقاء وجميع التوتات الزرقاوات هي ‪ ،Lazzies‬إذن ‪ Bloops‬هي بالتأكيد ‪Lazzies‬؟‬
‫خطأ‬
‫صحيح‬
‫‪ )32‬اختر الكلمة األكثر مماثلة ل"جدير بالثقة"‪:‬‬
‫حازم‬
‫عناد‬
‫ذو صلة‬
‫موثوق‬
‫وقح‬
‫‪ )33‬إذا رتبت هذه الحروف "ناتلرجإ" حصلتم على اسم ‪:‬‬
‫حيوان‬
‫بلد‬
‫دولة‬
‫مدينة‬
‫المحيط‬
‫‪ )34‬وهو واحد من أرقام التالية ال تنتمي إلى السلسلة ؟‬
‫‪48 – 29 – 26 – 33 – 33 – 5 – 2 – 3‬‬
‫‪0‬‬
‫‪5‬‬
‫‪16‬‬
‫‪18‬‬
‫‪19‬‬
‫‪ )35‬حمزة يحب ‪ 25‬ولكن ليس ‪ .24‬يحب ‪ 433‬ولكن ليس ‪333‬؛ يحب ‪ 344‬ولكن ليس ‪ .345‬ما هو الرقم الذي ال يحب؟‪:‬‬
‫‪01‬‬
‫‪51‬‬
‫‪011‬‬
‫‪111‬‬
‫‪0611‬‬
‫‪ )36‬كم عدد المربعات أو المسستطيالت التي تظهر في الرسم البياني أدناه ؟‬
‫‪01‬‬
‫‪06‬‬
‫‪11‬‬
‫‪18‬‬
‫‪15‬‬
‫‪ )37‬ما هو العدد المفقود في السلسلة أدناه؟‬
‫‪ - -27 - - 8 -- 3‬؟ ‪236 --325 --‬‬
‫‪30‬‬
‫‪15‬‬
‫‪36‬‬
‫‪16‬‬
‫‪99‬‬
‫‪61‬‬
‫‪ )38‬ماهو واحد من األمور التالية هي األقل مثل اآلخرين؟‬
‫قصيدة‬
‫رواية‬
‫تمثال‬
‫لوحة‬
‫‪ )39‬أي من الرسومات تحت تكمل سلسلة؟‬
‫؟‬
‫‪ )23‬أي من الرسومات تحت تكمل السلسلة؟‬
‫؟‬
‫‪31‬‬
‫زهرة‬
Appendix C:
L = LOW
M = MEDIUM
G = GOOD
E = EXCELLENT
Y = YES
N = NO
Class A
* Gender
1
F
2
F
3
F
4
F
5
F
6
F
7
F
8
F
9
F
10
F
11
F
12
F
13
F
Age
9
10
10
10
10
11
9
9
10
10
10
10
10
IQ
107
88
93
107
89
88
99
93
104
92
104
101
104
Sleep
8--10
8--10
8--10
8--10
8--10
8--10
8--10
8--10
8--10
8--10
8--10
8--10
8--10
Sport
1--3
1--3
3+
3+
3+
1--3
3+
3+
1--3
1--3
1--3
1--3
1--3
Breakfasters
Y
N
N
N
Y
Y
N
N
Y
Y
Y
Y
Y
Grades
E
M
G
M
M
L
G
G
G
G
M
G
G
Problems at
home
N
N
N
N
N
N
Y
N
N
N
N
N
N
TV
1--3
5+
-1
-1
5+
5+
1--3
5+
-1
1--3
1--3
-1
-1
Parentless
N
N
N
Y
N
N
N
N
N
N
N
N
14
15
16
17
F
F
F
M
10
10
10
11
99
85
106
99
8--10
8--10
8--10
8--10
1--3
1--3
1--3
1--3
Y
Y
Y
Y
E
G
G
M
Y
N
N
N
1--3
1--3
1--3
1--3
N
N
N
N
18
M
10
93
8--10
3+
Y
M
N
-1
N
19
M
8
115
8--10
1--3
Y
E
N
3--5
N
20
M
9
109
8--10
3+
N
G
N
5+
N
21
M
11
114
8--10
3+
Y
M
N
5+
N
22
23
24
25
26
27
M
M
M
M
M
M
11
9
11
11
11
8
94
93
33
43
91
96
8--10
8--10
8--10
8--10
8--10
8--10
1--3
1--3
3+
3+
3+
3+
Y
Y
Y
Y
N
Y
M
G
G
M
M
M
N
N
N
N
N
N
1--3
1--3
-1
5+
5+
5+
N
N
N
N
N
28
M
10
92
8--10
3+
N
E
Y
5+
N
32
29
M
11
93
8--10
3+
Y
M
N
-1
N
30
31
32
33
34
35
M
M
M
M
M
M
11
11
10
11
11
11
92
89
85
99
92
92
8--10
8--10
8--10
8--10
8--10
8--10
3+
1--3
1--3
1--3
3+
1--3
N
Y
N
Y
N
Y
L
G
E
G
G
M
N
N
Y
N
Y
N
-1
-1
1--3
-1
5+
5+
N
N
N
N
N
N
36
37
M
M
11
9
103
104
8--10
8--10
3+
3+
Y
Y
E
M
N
Y
3--5
3--5
Age
10
9
10
11
9
9
10
10
9
10
10
8
10
IQ
104
103
106
96
105
103
99
87
82
81
109
31
95
Sleep
6--8
8--10
6--8
6--8
8--10
6--8
8--10
8--10
6--8
8--10
8--10
8--10
8--10
Sport
3+
3+
3+
3+
3+
3+
3+
3+
3+
3+
3+
3+
3+
Breakfasters
Y
N
Y
Y
Y
Y
Y
Y
N
Y
Y
Y
Y
Grades
M
M
L
M
G
M
L
L
G
M
L
M
G
Problems at
home
N
Y
N
N
N
N
Y
N
N
N
Y
N
N
TV
-1
1--3
1--3
-1
-1
-1
1--3
1--3
1--3
3--5
1--3
-1
-1
Parentless
N
Y
N
N
N
N
N
N
N
Appendix D:
Class B
* Gender
1
F
2
F
3
M
4
M
5
M
6
M
7
M
8
M
9
M
10
M
11
M
12
M
13
M
N
N
N
14
15
16
17
M
M
M
M
11
10
10
11
31
104
119
104
8--10
8--10
8--10
6--8
1--3
3+
3+
3+
N
Y
Y
Y
L
M
M
M
N
N
N
N
1--3
-1
-1
1--3
N
N
N
N
18
M
11
107
6--8
3+
Y
G
N
1--3
N
19
M
9
106
8--10
3+
Y
G
N
1--3
N
20
M
10
91
6--8
3+
N
M
N
5+
N
21
M
10
88
8--10
3+
Y
M
Y
1--3
N
22
23
24
M
M
F
11
11
8
92
94
109
10+
10+
10+
3+
3+
3+
Y
Y
Y
L
M
M
N
N
N
5+
5+
3--5
N
N
Y
33
25
26
27
F
F
F
8
10
10
117
99
102
10+
3--6
6--8
3+
3+
3+
Y
Y
Y
M
M
M
Y
N
N
1--3
1--3
1--3
Y
N
N
28
F
9
97
8--10
3+
Y
M
N
1--3
N
29
F
8
100
6--8
3+
Y
M
N
1--3
N
30
31
32
33
34
35
F
F
F
F
F
F
9
9
8
9
9
9
119
102
111
105
103
112
6--8
6--8
8--10
8--10
8--10
8--10
3+
3+
3+
3+
3+
3+
N
Y
Y
Y
Y
Y
M
M
M
M
M
M
N
N
N
N
N
N
-1
-1
-1
1--3
-1
-1
N
N
N
N
N
N
36
37
F
F
10
11
104
107
8--10
6--8
1--3
3+
Y
Y
M
M
N
Y
-1
-1
N
Y
Appendix E: Multiple Regression Data
PROBABILITY
OUTPUT
RESIDUAL OUTPUT
Observation Predicted IQ
1
103.1732
2
103.4205
3
100.2798
4
97.38643
5
108.3995
6
100.2798
7
100.2798
8
98.15316
9
95.25975
10
100.2388
11
100.2388
12
100.2388
13
92.71075
14
95.60415
15
92.71075
16
81.72828
17
89.60069
18
89.60069
19
86.74837
20
86.74837
Residuals
3.826754
-4.42051
-8.27984
6.613568
-9.39951
-15.2798
5.720161
-5.15316
11.74025
3.761246
0.761246
3.761246
0.289255
-62.6042
0.289255
10.27172
-0.60069
9.399312
12.25163
7.251635
Percentile
0.675676
2.027027
3.378378
4.72973
6.081081
7.432432
8.783784
10.13514
11.48649
12.83784
14.18919
15.54054
16.89189
18.24324
19.59459
20.94595
22.2973
23.64865
25
26.35135
34
IQ
31
31
33
43
81
82
85
85
87
88
88
88
89
89
91
91
92
92
92
92
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
89.64177
89.67238
92.57626
98.57973
98.01918
87.63834
92.834
92.834
84.74494
92.834
95.75802
92.86461
86.83053
98.27641
89.37954
103.4721
94.57519
105.4322
97.34316
105.4322
110.6585
86.82835
87.85718
90.75059
92.79291
101.3476
91.94183
95.08469
89.85842
89.63958
95.08469
75.76589
94.83524
97.72865
95.64524
97.97809
103.3899
103.3899
100.5271
105.4733
3.358227
-4.67238
22.42374
4.42027
5.980821
21.36166
21.166
-49.834
6.255063
3.166
-3.75802
-0.86461
5.169466
-5.27641
-1.37954
-14.4721
-6.57519
-1.43222
21.65684
-3.43222
-3.65849
4.171654
4.142818
3.24941
-11.7929
7.652428
14.05817
3.915313
-2.85842
-7.63958
13.91531
-44.7659
9.16476
9.271353
10.35476
-9.97809
-6.3899
1.610103
2.472902
-3.47331
27.7027
29.05405
30.40541
31.75676
33.10811
34.45946
35.81081
37.16216
38.51351
39.86486
41.21622
42.56757
43.91892
45.27027
46.62162
47.97297
49.32432
50.67568
52.02703
53.37838
54.72973
56.08108
57.43243
58.78378
60.13514
61.48649
62.83784
64.18919
65.54054
66.89189
68.24324
69.59459
70.94595
72.2973
73.64865
75
76.35135
77.7027
79.05405
80.40541
35
92
92
93
93
93
93
93
94
94
95
96
96
97
99
99
99
99
99
99
100
101
102
102
103
103
103
103
104
104
104
104
104
104
104
104
105
105
106
106
106
61
62
63
64
65
66
67
68
69
70
71
72
73
74
105.4733
107.5567
106.5328
103.3488
103.3488
94.79416
95.60415
94.79416
92.71075
95.60415
92.71075
92.71075
103.3488
97.34535
-5.47331
-8.55672
10.46725
7.651188
-0.34881
1.205845
9.395847
8.205845
-61.7107
-0.60415
11.28925
26.28925
8.651188
6.654653
81.75676
83.10811
84.45946
85.81081
87.16216
88.51351
89.86486
91.21622
92.56757
93.91892
95.27027
96.62162
97.97297
99.32432
36
107
107
107
107
109
109
109
111
112
114
115
117
119
119